Supervised Incremental Learning Algorithm

A Supervised Incremental Learning Algorithm is a supervised learning algorithm based on incremental learning.

• AKA: Supervised Online Learning Algorithm.
• Context:
  • It can be implemented by a Supervised Incremental Learning System to solve a Supervised Incremental Learning Task.
• Example(s):
  • the algorithm proposed in Utgoff (2017),
  • …
• Counter-Example(s):
  • a Reinforcement Learning Algorithm.
  • a No-Regret Online Learning Algorithm.
  • a Contextual Bandits Algorithm.
  • a Batch Learning Algorithm.
• See: Online Machine Learning Algorithm, Winnow Algorithm, Online Processing, Data Stream, Evolutionary Learning Algorithm.

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References

2017a

• (Utgoff, 2017) ⇒ Paul E. Utgoff. (2017). Incremental Learning. In: Sammut, C., Webb, G.I. (eds) Encyclopedia of Machine Learning and Data Mining. Springer, Boston, MA
  • QUOTE: Incremental learning refers to any online learning process that learns the same model as would be learned by a batch learning algorithm.

2017b

• (Auer, 2017) ⇒ Peter Auer. (2017). "Online Learning". In: (Sammut & Webb, 2017).
  • QUOTE: Online learning and its variants are one of the main models of computational learning theory, complementing statistical PAC learning and related models. An online learner needs to make predictions about a sequence of instances, one after the other, and receives feedback after each prediction. The performance of the online learner is typically compared to the best predictor from a given class, often in terms of its excess loss (the regret) over the best predictor. Some of the fundamental online learning algorithms and their variants are discussed: weighted majority, follow the perturbed leader, follow the regularized leader, the perceptron algorithm, the doubling trick, bandit algorithms, and the issue of adaptive versus oblivious instance sequences. A typical performance proof of an online learning algorithm is exemplified for the perceptron algorithm.

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